Business objectives clash with data quality standards. How can you navigate this challenging dilemma?
When business objectives seem to conflict with maintaining high data quality standards, finding a middle ground is key. Consider these strategies to strike a balance:
- Assess and align priorities, ensuring that critical data quality is non-negotiable.
- Implement robust data governance policies to maintain standards without hindering objectives.
- Regularly review objectives and data practices, adapting as necessary to minimize conflict.
How do you reconcile business targets with the need for accurate data? Share your strategies.
Business objectives clash with data quality standards. How can you navigate this challenging dilemma?
When business objectives seem to conflict with maintaining high data quality standards, finding a middle ground is key. Consider these strategies to strike a balance:
- Assess and align priorities, ensuring that critical data quality is non-negotiable.
- Implement robust data governance policies to maintain standards without hindering objectives.
- Regularly review objectives and data practices, adapting as necessary to minimize conflict.
How do you reconcile business targets with the need for accurate data? Share your strategies.
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Balancing business objectives and data quality standards often leads to tensions that need to be carefully addressed by all stakeholders... Prioritize critical data areas: Determine which data sets directly impact business objectives and focus quality standards on those areas. In this way, objectives can be aligned without overstretching resources. Cross-functional collaboration: Involve both the data and business teams in setting realistic quality benchmarks. Understanding each other's pressures can defuse conflict and encourage compromise. Iterative reviews: Establish checkpoints to re-evaluate data quality against goals and ensure standards evolve with changing business needs without compromising the integrity of key data.
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It’s only a conflict if we see it from the wrong angle. A proven three-step approach focuses data quality on business impact: 1️⃣ Align Data Quality with Business Goals Educate teams that poor data quality devalues data assets, such as asset depreciation. Show how data quality helps them achieve their current business goals. 2️⃣ Operationalise Standards Separate non-negotiable requirements from ideal targets. Explain to business teams why these standards matter to them. If it’s not tied to business value, reconsider. 3️⃣ Set Minimum Standards & Foster Competition Use a dashboard to track data quality across teams. Transparency and a little competition drive engagement and improvement.
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